How do I install the Community Research Insight skill?

Run npx skillmds@latest add clawdotnet/community-research-insight in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Community Research Insight safe to use?

This skill has not completed SkillMD's automated safety review yet. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Community Research Insight?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Community Research Insight free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Community Research Insight?

clawdotnet (@clawdotnet) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Community Research Insight by clawdotnet

Name: Community Research Insight
Author: clawdotnet

Community Research Insight

Extract structured insight briefs from community research transcripts or notes. Produces pain points, stakeholder needs, opportunity maps, risks, and follow-up questions. Requires human review before publication.

clawdotnet 4e244a9 2.5 KB Updated Sep 17, 2026

File contents

Community Research Insight Extractor

Extracts pain points, stakeholder needs, risks, and practical technology opportunities from community-engaged research discussions. Produces a structured insight brief for human review before publication.

What It Does

Step	Kind	Purpose
`collect`	`user_input`	Collect transcript, context, and audience via chat
`analyze`	`llm_chat`	Extract grounded themes as structured JSON
`analyze_fallback`	`llm_chat`	Produce best-effort grounded JSON if primary analysis fails
`draft`	`llm_chat`	Draft the full 6-section insight brief as structured JSON
`validate`	`llm_chat`	Gate preview on PASS vs REVISE grounding validation
`validation_revise`	`llm_chat`	Explain why the brief is blocked when validation fails
`preview`	`llm_chat`	Render validated findings as human-readable Markdown
`review`	`user_input`	Pause for human approve/revise/reject decision
`final_response`	`llm_chat`	Produce final output based on review decision

Guardrails

Never invent quotes, names, dates, or statistics.
Never attribute views to named people unless present in the source.
Never recommend replacing community engagement with automation.
Always separate evidence from inference.
Always flag missing information rather than filling gaps.
Always require human review before publication or named attribution.

Fallback

If analyze fails (timeout, provider error, or JSON contract failure), analyze_fallback runs a single-turn llm_chat on the same transcript and must satisfy the same JSON output contract. If validate returns REVISE, the preview path is blocked and validation_revise explains what must be fixed before human review.

Output Contract

The analyze, analyze_fallback, and draft steps enforce OutputContract JSON validation. The draft step requires executive_summary, key_pain_points, stakeholder_needs, opportunity_map, risks_and_cautions, and follow_up_questions.

Safety

Outputs are decision-support drafts for human review. They are not final professional advice in research, policy, or community engagement contexts. Named attribution and external publication require explicit reviewer approval.

---name: community-research-insightdescription: Extract structured insight briefs from community research transcripts or notes. Produces pain points, stakeholder needs, opportunity maps, risks, and follow-up questions. Requires human review before publication.---# Community Research Insight ExtractorExtracts pain points, stakeholder needs, risks, and practical technologyopportunities from community-engaged research discussions. Produces a structuredinsight brief for human review before publication.## What It Does| Step | Kind | Purpose || --- | --- | --- || `collect` | `user_input` | Collect transcript, context, and audience via chat || `analyze` | `llm_chat` | Extract grounded themes as structured JSON || `analyze_fallback` | `llm_chat` | Produce best-effort grounded JSON if primary analysis fails || `draft` | `llm_chat` | Draft the full 6-section insight brief as structured JSON || `validate` | `llm_chat` | Gate preview on PASS vs REVISE grounding validation || `validation_revise` | `llm_chat` | Explain why the brief is blocked when validation fails || `preview` | `llm_chat` | Render validated findings as human-readable Markdown || `review` | `user_input` | Pause for human approve/revise/reject decision || `final_response` | `llm_chat` | Produce final output based on review decision |## Guardrails- **Never** invent quotes, names, dates, or statistics.- **Never** attribute views to named people unless present in the source.- **Never** recommend replacing community engagement with automation.- **Always** separate evidence from inference.- **Always** flag missing information rather than filling gaps.- **Always** require human review before publication or named attribution.## FallbackIf `analyze` fails (timeout, provider error, or JSON contract failure),`analyze_fallback` runs a single-turn `llm_chat` on the same transcript and mustsatisfy the same JSON output contract. If `validate` returns REVISE, the previewpath is blocked and `validation_revise` explains what must be fixed before humanreview.## Output ContractThe `analyze`, `analyze_fallback`, and `draft` steps enforce `OutputContract` JSONvalidation. The `draft` step requires `executive_summary`, `key_pain_points`,`stakeholder_needs`, `opportunity_map`, `risks_and_cautions`, and`follow_up_questions`.## SafetyOutputs are decision-support drafts for human review. They are **not** finalprofessional advice in research, policy, or community engagement contexts.Named attribution and external publication require explicit reviewer approval.

Frequently asked questions